The representation gap costs brands revenue
Women who wear US size 14 and above represent approximately 67% of the adult female population (NPD Group, 2023), yet the majority of fashion brand PDPs feature only straight-size models in sizes 2–8. The message this sends to plus-size shoppers is unambiguous: this brand hasn't considered whether their product works for your body. That perception drives abandonment, even when the garment is fully available in extended sizes.
The revenue impact is concrete. A study by the Coresight Research Group found that plus-size shoppers who encountered representation on a PDP converted at 28% higher rates than those who did not. Brands that offer extended sizing but don't visually represent it are leaving that conversion premium on the table — and often facing above-average return rates because shoppers are guessing how garments will fit.
Why traditional photography doesn't solve this
Photographing a garment on a representative spread of body types — say, sizes 2, 6, 10, 14, 18, 22, 26 — requires seven separate model shoots per SKU. For a catalog of 500 SKUs, that's 3,500 individual model-garment shoot combinations. Even large brands with significant photography budgets can't sustain this across their full catalog, especially for new-season drops where 100 new styles might arrive in a week.
The result is a well-intentioned but partial solution: brands photograph one or two plus-size models for hero campaign images but leave the majority of their catalog without extended-size representation. Shoppers who've seen inclusive branding in emails and social ads arrive at the PDP and find the same 5'11" size-4 model as everywhere else. The credibility gap this creates is real and measurable.
How AI try-on closes the gap at scale
Photta's AI generates a visualisation of any garment on the shopper's own body from a single uploaded photo. The model adapts to the person's actual proportions — height, weight distribution, torso length, hip-to-waist ratio — rather than fitting the garment to a standard shape and hoping for the best. A size 22 shopper sees the dress as it would actually drape on her body, not on a resized straight-size render.
Because the try-on is per-shopper rather than per-SKU, the catalog coverage problem disappears. Every product in your catalog instantly has 'model representation' for every shopper body type, because the model is the shopper. A 500-SKU catalog is fully inclusive for a size-24 shopper and a size-2 shopper simultaneously, without a single additional photo shoot.
Brand reputation effects of inclusive try-on
Brands that deploy inclusive virtual try-on see measurable brand sentiment improvement in plus-size shopper communities. Body-positive communities on TikTok, Reddit, and Instagram are highly vocal about brands that earn trust through genuine representation — and equally vocal about brands that signal inclusion without delivering it. A try-on experience that works beautifully across all body types generates organic advocacy.
Repeat purchase rates in the plus-size segment improve when shoppers have a confident first purchase. Photta cohort data shows that plus-size shoppers who used try-on on their first order with a brand have a 35% higher 90-day repeat purchase rate compared to those who didn't. The mechanism is simple: a good first-time fit experience, confirmed visually before purchase, builds brand trust that drives loyalty.
Implementing inclusive try-on without a reshooting budget
Photta requires no additional model photography to deliver inclusive representation. Your existing product photos — the same images already on your PDPs — are the input. The AI generates the shopper-specific visualisation at try-on time, not at photo-shoot time. The transition from exclusive to inclusive is a 30-second script tag installation, not a months-long photography project.
For brands building a genuine inclusive sizing strategy, Photta pairs well with extended-size fit notes (e.g. 'runs narrow in the shoulder — size up if 40"+ bust') added to each PDP. The try-on handles the visual confidence; the fit notes handle edge cases where body shape measurements diverge from the garment's intended construction. Together, they produce a PDP that plus-size shoppers actively recommend to their networks.